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检索条件"主题词=Multi-Step Learning Algorithms"

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Analysis of Fuzzy Cognitive Maps with multi-step learning algorithms in Valuation of Owner-Occupied Homes

Analysis of Fuzzy Cognitive Maps with Multi-Step Learning Al...

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IEEE International Conference on Fuzzy Systems

作者： Poczeta, Katarzyna Yastrebov, Alexander Kielce Univ Technol Dept Comp Sci Applicat Kielce Poland

ISBN: (纸本)9781479920723

In the paper some analysis of multi-step learning algorithms for fuzzy cognitive map (FCM) is given. FCMs, multi-step supervised learning based on gradient method and unsupervised one based on nonlinear Hebbian learning (NHL) algorithm are described. Comparative analysis of these methods to one-step algorithms, from the point of view of the speed of convergence of learning algorithm and the influence on the decision support system for the valuation of owner-occupied homes was performed. Simulation results were obtained with the use of ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. The results show that the implementation of the multi-step technique gives certain possibilities to get quicker values of target FCM relations and improve the operation of the learned system.

关键词： Fuzzy Cognitive Maps multi-step learning algorithms Gradient Method Nonlinear Hebbian learning Decision Support System

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Analysis of Fuzzy Cognitive Maps with multi-step learning algorithms in Valuation of Owner-Occupied Homes

Analysis of Fuzzy Cognitive Maps with Multi-Step Learning Al...

引用

IEEE International Conference on Fuzzy Systems

作者： Katarzyna Poczeta Alexander Yastrebov Department of Computer Science Applications Kielce University of Technology

ISBN: (纸本)9781479920747

关键词： Fuzzy Cognitive Maps multi-step learning algorithms Gradient Method Nonlinear Hebbian learning Decision Support System

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Application of Fuzzy Cognitive Maps to water demand prediction

Application of Fuzzy Cognitive Maps to water demand predicti...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Papageorgiou, Elpiniki I. Poczeta, Katarzyna Laspidou, Chrysi CERTH Ctr Res & Technol Hellas Inst Informat Technol 6th Km Charilaou Thermi Rd Thermi 57001 Greece Technol Educ Inst TEI Cent Greece Dept Comp Engn Lamia 35100 Greece Kielce Univ Technol Dept Comp Sci Applicat Kielce Poland Univ Thessaly Dept Civil Engn Volos 38334 Volos Greece

ISBN: (纸本)9781467374286

This article is focused on the issue of learning of Fuzzy Cognitive Maps designed to model and predict time series. The multi-step supervised-learning based-on-gradient methods as well as population-based learning, with the use of real coded genetic algorithms, are described. In this study, a new structure optimization genetic algorithm for fuzzy cognitive maps learning is proposed for automatic construction of FCM applied to time series prediction. The proposed learning methodologies are based on an FCM reconstruction procedure using historical time series. The main contribution of this study is the analysis of the use of FCMs with their learning algorithms based on the multi-step gradient method (MGM) and other population-based methods to predict water demand. The performance of learning algorithms is presented through the analysis of real data of daily water demand and the corresponding prediction. The multivariate analysis of historical water demand data is held for five variables, mean and high temperature, precipitation, wind speed and touristic activity. Simulation results were obtained with the ISEMK (Intelligent Expert System based on Cognitive Maps) software tool. Through the experimental analysis, we demonstrate the usefulness of the new proposed FCM learning algorithm in water demand prediction, by calculating the known prediction errors. The advantage of the optimization genetic algorithm structure is its ability to select the most significant relations between concepts for prediction.

关键词： Fuzzy Cognitive Maps multi-step learning algorithms Population-Based learning algorithms Real Coded Genetic Algorithm Structure Optimization Genetic Algorithm Gradient Method

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